We offer a broad, newly updated view on user attributes in recommendation, by reporting on a set of experiments that make three main contributions.
First, we demonstrate that user attributes are not always helpful to improve recommender prediction performance (Section ).
This point may not be surprising in light of the well-known disappointment of item attributes [1]}.
However, many papers studying side information combine item and user side information [2]}, [3]}, [4]}, rather than separating out user attributes as we do.
Second, we show that user attributes actually have the potential to harm recommendation when we look beyond prediction performance to metrics like coverage and diversity (Section ).
Third, we study whether user attributes survive from the training data into the recommender system output.
We establish that there is a weak but consistent user signal in recommendation lists that can be detected by a machine learning classifier (Section ).
Interestingly, adding user side information can amplify this signal without actually helping the recommender, opening new research questions for future work.
